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Previous studies have described significant impact of different types of noise on the linear behavior of heart rate variability (HRV). However, there are few studies regarding the complexity of HRV during exposure to traffic noise. In this study, we evaluated the complexity of HRV during traffic noise exposure. We analyzed 31 healthy female students aged between 18 and 30 years. Volunteers remained at rest seated under spontaneous breathing during 10 minutes with an earphone turned off, and then they were exposed to traffic noise through an earphone for a period of 10 minutes. The traffic noise was recorded from a very busy city street and the sound was comprised of car, bus, and trucks engines and horn (71–104 dB). We observed no significant changes in the linear analysis of HRV. CFP3 (Cohen’s

Noise may be considered an unpleasant sound, which may have effects on physiological variables. It is often found in hazardous situations due to industrialization and urbanization [

The linear analysis of HRV in the time and frequency domains is not entirely suitable to provide information about the complex dynamics of heartbeat origination. This is because the mechanisms involved in cardiovascular physiology interact with each other in a nonlinear way [

Most recently, the European Society of Cardiology together with the European Heart Rhythm Association and coendorsed by the Asia Pacific Heart Rhythm Society drew attention to nonlinear methods for assessing HRV [

This information related to chaos theory, fractal mathematics, and the dynamic complexity of HRV has not yet been fully applied in medical practice clinically. Yet, it is a productive area for research and development of knowledge in both health and disease [

We examined 31 apparently healthy female students aged between 18 and 30 years. All volunteers were informed about the procedures and objectives of the study and, after agreeing, signed a confidential consent form. All study procedures were approved by the Research Ethics Committee (REC) of the institution (case number 2011/382) and followed the Resolution 196/96 of the National Health Council. We excluded women under the following conditions: body mass index (BMI) > 30 kg/m^{2}, systolic blood pressure (SBP) > 140 mmHg or diastolic blood pressure (DBP) > 90 mmHg (at rest), and endocrine, cardiovascular, respiratory, and neurological related disorders or any condition that prevented the subject from performing the study. In order to avoid effects related to sexual hormones, we did not include women on the 11th to 15th and 21st to 25th days after the first day of the menstrual cycle [

The subjects were identified by collecting the following information: age, mass, height, and body mass index (BMI). Mass was measured using a digital scale (W200/5, Welmy, Brazil) with a precision of 0.1 kg. Height was determined using a stadiometer (ES2020, Sanny, Brazil) with a precision of 0.1 cm and being 220 cm long. The body mass index (BMI) was calculated by the subsequent formula: mass (kg)/height (m^{2}). We measured heart rate and blood pressure. Heart rate was measured with the Polar RS800CX heart rate monitor (Polar Electro, Finland). Blood pressure was indirectly measured by auscultation through calibrated aneroid sphygmomanometer (Welch Allyn, New York, USA) and stethoscope (Littmann, St. Paul, USA) with all subjects seated.

The measurements of equivalent sound levels were performed in a soundproofed room, using an audio dosimeter SV 102 (Svantek, Finland). It was programmed in measuring circuit 7 in “A” weighting, slow response [

We used the MIRE earphone, which was placed inside the auditory canal of the subject and linked to a personal stereo. Prior to each measurement, the earphones were calibrated with the acoustic calibrator CR: Model 514 (Cirrus Research plc).

This tool was used to analyze the Leq (A), which is defined as the equivalent sound pressure level, and the sound level corresponds to the same constant time interval. It contained the same total sound energy, which also analyzed the spectrum of sound stimulation (eighth track) frequency [

Equivalent sound level.

Data collection was commenced at room temperature between 21°C and 25°C and with humidity between 50% and 60%. The subjects were instructed not to ingest alcohol or caffeine for 24 hours prior to evaluation. The data collection was achieved individually between 18:00 and 21:00 to avoid circadian influences. The volunteers were instructed to remain at rest and avoid conversation during the experiment.

After the initial evaluation, the heart monitor belt was placed over the thorax, aligned with the distal third of the sternum and the Polar RS800CX heart rate receiver (Polar Electro, Finland) was placed on the wrist. Subsequently, the volunteers remained at rest seated for 10 minutes with the headset off.

Next, the volunteers were exposed to traffic noise through an earphone for a period of 10 minutes. The traffic noise was recorded from a very busy street in Marília city, SP, Brazil. The sounds were produced by cars, buses, trucks engineers, and horns.

The RR intervals were recorded by the Polar RS800CX heart rate monitor with a sampling rate of 1000 Hz. They were then transferred to the Polar Precision Performance software (v. 3.0, Polar Electro, Finland). This software allowed the visualization of the HR and the extraction of a file relating to a cardiac period (RR-interval) in a “txt” file. After digital filtering supplemented with manual filtering to eliminate artefacts and premature ectopic beats, 500 RR intervals were applied for data analysis. Only series with more than 95% of sinus beats were included in the study. HRV was analyzed before and during traffic noise.

The time domain analysis was accomplished in terms of SDNN (standard deviation of normal-to-normal RR intervals), pNN50 (percentage of adjacent RR intervals with a difference of duration greater than 50 milliseconds), and RMSSD (root-mean square of differences between adjacent normal RR intervals in a time interval) [

To obtain the spectral indexes for HRV analysis in the frequency domain, the frequency recordings underwent mathematical processing, thus generating a tachogram that expressed the variation of RR intervals as a function of time. The tachogram contained a signal that varied with time and was processed by the mathematical Fast Fourier Transform (FFT) algorithm. Welch’s periodogram method based on FFT using a window width of 256 seconds and an overlap of 50% was applied.

Low frequency (LF, ranging between 0.04 and 0.15 Hz) and high frequency (HF, ranging from 0.15 to 0.4 Hz) spectral components were selected in normalized units (nu). The ratio between these components in absolute values (LF/HF) represents the relative value of each spectral component in relation to the total potential minus the very low frequency (VLF) components. It is important to mention that the LF/HF index may provide significant information on autonomic regulation of sinus node under controlled conditions and short-term recordings [

For computation of the linear indices, we applied the HRV analysis software (Kubios HRV v.1.1 for Windows, Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland).

Statistical methods of the linear indices were approved for the computation of means and standard deviations. Normal Gaussian distribution of the data was verified by the Shapiro-Wilk goodness-of-fit test (

To enable a comparison of the variables between control and traffic noise exposure, we applied the unpaired Student

Detrended fluctuation analysis (DFA) [

The integrated time series was then divided into equally sized and nonoverlapping windows of length

The root-mean square fluctuation

DFA is a technique extensively imposed in variability analysis. It has been applied to the evaluation of posture [

Multitaper Method (MTM) [

A MTM power spectrum of a time series of 500 RR intervals in a traffic noise exposure subject. sMTM is the area beneath the spectrum, yet above the baseline created by broadband noise as the signal becomes chaotic.

As stated before, the DFA [

Spectral Multitaper Method (sMTM) [

The parameters (CFP 1–7) are referred to as chaotic forward parameters (CFP) for the functions CFP1 to CFP7 below where they are applied to normal and traffic noise exposure subjects’ RR-interval time series. Since

Shannon entropy [

Shannon entropy may be used globally, applying to the time series wholly or nearby around specific points. This measure can provide extra evidence about specific events such as outliers or intermittent events. In contrast to Tsallis [

Renyi entropy is a general statement of Shannon entropy that is dependent on a specified parameter. Renyi entropy depends on the entropic order

Tsallis entropy is a general statement of the standard Shannon-Boltzmann-Gibbs entropy. It was introduced in the application of statistical mechanics and is used in computer sciences for pattern recognition. Tsallis entropy is dependent on the specified parameter termed entropic index

Approximate Entropy (ApEn) was discussed by Pincus [

A minimum value of zero for ApEn would indicate a totally predictable time series, while a maximum value of one would specify an entirely unpredictable time series. Most of the time, the values are between these two values.

ApEn is mathematically described as in the Kubios HRV Analysis Manual [

First a set of length

The distance between these vectors is the maximum absolute difference between the corresponding elements; hence,

Next for each

Due to the normalization, the value of

Finally, the ApEn is obtained as

Sample entropy (SampEn) [

SampEn is also described as in the Kubios HRV Analysis Manual [

In SampEn, the self-comparison of

Now the value of

SampEn is described mathematically as

Fractal systems exhibit a characteristic termed self-similarity. A self-similar object upon close examination is comprised of smaller versions of itself. There are several algorithms which can be applied to measure fractal dimension. There are those by Higuchi [

Higuchi derived this new algorithm to measure the fractal dimension of discrete time sequences. It is a technique that is enforced directly to the RR intervals. There is no power spectrum step involved. As the reconstruction of the attractor phase space is unnecessary, the algorithm is simpler and faster than the Correlation Dimension [

It is based on a measure of length,

The curve is said to show fractal dimension

From a given time series,

For each of the time series

Finally, the slope of the curve

To quantify the magnitude of difference between protocols for significant differences, the effect size was calculated using Cohen’s

Table

Body mass index (BMI), age, height, and mass of the volunteers. m: meters; kg: kilograms; bpm: beats per minute; ms: milliseconds; mmHg: millimeters of mercury.

Variable | Value |
---|---|

Age (years) | |

Height (m) | |

Mass (kg) | |

BMI (kg/m^{2}) | |

According to Figures

Mean heart rate and HRV analysis before (control) and during traffic noise exposure. pNN50: the percentage of adjacent RR intervals with a difference of duration greater than 50 ms; RMSSD: root-mean square of differences between adjacent normal RR intervals in a time interval; SDNN: standard deviation of normal-to-normal RR intervals; HR: heart rate;

HRV analysis before (control) and during traffic noise exposure. LF: low frequency; HF: high frequency; LF/HF: low frequency/high frequency ratio; n.u.: normalized units; ms: milliseconds; HR: heart rate;

In Table

Mean values and standard deviation for the chaotic forward parameters (CFP) for the normal and traffic noise exposure subjects.

Chaotic global | Mean ± SD | Mean ± SD | ANOVA1 | Kruskal-Wallis | Effect size |
---|---|---|---|---|---|

CFP1 | | | | | - |

CFP2 | | | | | 0.53 (medium) |

CFP3 | | | <0.0001 | <0.0001 | 1.28 (large) |

CFP4 | | | | | - |

CFP5 | | | | | 0.65 (medium) |

CFP6 | | | <0.0001 | <0.0001 | 1.11 (large) |

CFP7 | | | | | 0.46 (small) |

The boxplots illustrate the values of chaotic forward parameters one to seven (CFP1 to CFP7) for control (a) and traffic noise exposure (b) subjects with 500 RR intervals throughout. The point closest to the zero is the minimum and the point farthest away is the maximum. The boundary of the box closest to zero indicates the 25th percentile, a line within the box marks the median (not the mean), and the boundary of the box farthest from zero indicates the 75th percentile. The difference between these points is the interquartile range (IQR). Whiskers (or error bars) above and below the box indicate the 90th and 10th percentiles, respectively.

There are seven permutations of the three chaotic global parameters. All chaotic global values have equal weighting. The chaotic forward parameter (CFP) enables different combinations of chaotic globals to be applied to ensure that we have the best combination to be verified later by a multivariate analysis. It is anticipated that the CFP which applies all three should be the most robust. This is because it takes the information and processes it in three different ways. The summation of the three would be expected to deviate greater than single or double permutations. The potential analytical hazard here is that since we are only calculating spectral components, the phase information is lost.

When implementing parametric statistics, normal distribution of data is assumed. To test this assumption, we apply the Anderson-Darling and Lilliefors tests. In the case of the Anderson-Darling test, an empirical cumulative distribution function is applied, while the Lilliefors test is beneficial when the number of subjects is low. The results from both tests reveal similar numbers of nonnormal and normal distributions, so we apply both the Kruskal-Wallis and ANOVA1 tests of significance.

Principal Component Analysis (PCA) is a multivariate technique for analyzing the complexity of high-dimensional datasets. PCA is useful when

The plot illustrates the component loadings CFP1 to CFP7 for the 500 RR intervals of 31 traffic noise exposure subjects. The CFP values are deduced by using the MTM spectra throughout. The properties of the MTM spectra are as follows: sampling frequency 1 Hz, DPSS of 3, FFT length of 256, and Thomson’s nonlinear combination at “adaptive.” CFP1 and CFP3 are the most influential components when assessed by PCA.

CFP1t has the First Principal Component (PC1) of 0.358 and the Second Principal Component (PC2) of

Table

Principal Component Analysis for CFPt for 7 groups of 31 traffic noise exposure subjects.

Chaotic global | PC1 | PC2 |
---|---|---|

CFP1t | 0.358 | −0.406 |

CFP2t | 0.066 | −0.577 |

CFP3t | 0.191 | −0.540 |

CFP4t | 0.490 | 0.086 |

CFP5t | 0.446 | 0.253 |

CFP6t | 0.494 | −0.023 |

CFP7t | −0.384 | −0.371 |

The descriptive statistics of the Higuchi fractal dimension from the control subjects (

Higuchi fractal dimension statistics through

Property | Higuchi fractal dimension | |||||||
---|---|---|---|---|---|---|---|---|

| Mean | SE mean | StDev | Minimum | | Median | | Max |

10 | 1.6768 | 0.0309 | 0.1722 | 1.1992 | 1.5644 | 1.6971 | 1.7999 | 1.9369 |

20 | 1.7446 | 0.0274 | 0.1526 | 1.2605 | 1.7057 | 1.7664 | 1.8479 | 1.9496 |

30 | 1.7783 | 0.0259 | 0.1441 | 1.3436 | 1.7341 | 1.8055 | 1.8879 | 1.9604 |

40 | 1.8017 | 0.0253 | 0.1408 | 1.3984 | 1.7661 | 1.8355 | 1.9092 | 1.9723 |

50 | 1.8194 | 0.0242 | 0.1350 | 1.4385 | 1.7921 | 1.8589 | 1.9115 | 1.9709 |

60 | 1.8333 | 0.0233 | 0.1295 | 1.4514 | 1.8088 | 1.8785 | 1.9219 | 1.9711 |

70 | 1.8436 | 0.0224 | 0.1246 | 1.4686 | 1.8302 | 1.8824 | 1.9283 | 1.9688 |

80 | 1.8509 | 0.0216 | 0.1205 | 1.4868 | 1.8348 | 1.8883 | 1.9298 | 1.9664 |

90 | 1.8573 | 0.0211 | 0.1173 | 1.5063 | 1.8488 | 1.8995 | 1.9315 | 1.9675 |

100 | 1.8618 | 0.0207 | 0.1151 | 1.5267 | 1.8565 | 1.9007 | 1.9334 | 1.9702 |

110 | 1.8659 | 0.0203 | 0.1133 | 1.5473 | 1.8655 | 1.9080 | 1.9344 | 1.9697 |

120 | 1.8709 | 0.0203 | 0.1128 | 1.5659 | 1.8799 | 1.9131 | 1.9358 | 1.9705 |

130 | 1.8760 | 0.0202 | 0.1126 | 1.5682 | 1.8928 | 1.9216 | 1.9388 | 1.9695 |

140 | 1.8808 | 0.0201 | 0.1119 | 1.5743 | 1.8944 | 1.9298 | 1.9437 | 1.9715 |

150 | 1.8852 | 0.0201 | 0.1117 | 1.5769 | 1.8915 | 1.9311 | 1.9484 | 1.9755 |

The descriptive statistics of the Higuchi fractal dimension from the traffic noise exposure subjects (

Higuchi fractal dimension statistics through

Property | Higuchi fractal dimension statistics (traffic noise exposure) | |||||||
---|---|---|---|---|---|---|---|---|

| Mean | SE mean | StDev | Minimum | | Median | | Max |

10 | 1.6971 | 0.0279 | 0.1555 | 1.2606 | 1.6284 | 1.7171 | 1.8040 | 1.9496 |

20 | 1.7644 | 0.0265 | 0.1477 | 1.2952 | 1.7010 | 1.8077 | 1.8462 | 1.9383 |

30 | 1.7898 | 0.0256 | 0.1428 | 1.3544 | 1.7507 | 1.8447 | 1.8762 | 1.9450 |

40 | 1.8082 | 0.0242 | 0.1347 | 1.3912 | 1.7775 | 1.8533 | 1.8811 | 1.9579 |

50 | 1.8240 | 0.0227 | 0.1264 | 1.4132 | 1.8103 | 1.8590 | 1.9000 | 1.9703 |

60 | 1.8358 | 0.0217 | 0.1208 | 1.4290 | 1.8232 | 1.8741 | 1.9082 | 1.9691 |

70 | 1.8446 | 0.0214 | 0.1193 | 1.4368 | 1.8208 | 1.8879 | 1.9161 | 1.9722 |

80 | 1.8507 | 0.0212 | 0.1178 | 1.4372 | 1.8269 | 1.8934 | 1.9140 | 1.9762 |

90 | 1.8568 | 0.0210 | 0.1171 | 1.4337 | 1.8414 | 1.8922 | 1.9194 | 1.9804 |

100 | 1.8613 | 0.0209 | 0.1163 | 1.4326 | 1.8497 | 1.8990 | 1.9243 | 1.9837 |

110 | 1.8660 | 0.0207 | 0.1151 | 1.4383 | 1.8569 | 1.9076 | 1.9267 | 1.9824 |

120 | 1.8694 | 0.0203 | 0.1130 | 1.4474 | 1.8613 | 1.9121 | 1.9286 | 1.9811 |

130 | 1.8727 | 0.0200 | 0.1115 | 1.4597 | 1.8701 | 1.9103 | 1.9338 | 1.9831 |

140 | 1.8769 | 0.0197 | 0.1099 | 1.4732 | 1.8809 | 1.9070 | 1.9397 | 1.9810 |

150 | 1.8806 | 0.0193 | 0.1075 | 1.4888 | 1.8854 | 1.9066 | 1.9380 | 1.9795 |

Figure

Box-and-whiskers plot for Higuchi fractal dimension of RR intervals of the control subjects (a) and the traffic noise exposure subjects (b), calculated multiple times from 10 to 150 in equidistant units for different levels of

The levels of significance for parametric ANOVA1 and nonparametric Kruskal-Wallis test of significance for values of the Higuchi fractal dimension at varying levels of

Higuchi fractal dimension at varying levels of

Property | Higuchi fractal dimension statistics (control versus traffic) | |
---|---|---|

| ANOVA1 ( | Kruskal-Wallis ( |

10 | 0.6269 | 0.6322 |

20 | 0.6059 | 0.5543 |

30 | 0.7534 | 0.6523 |

40 | 0.8539 | 0.9215 |

50 | 0.8904 | 0.8880 |

60 | 0.9367 | 1.0000 |

70 | 0.9742 | 0.9215 |

80 | 0.9940 | 0.8108 |

90 | 0.9855 | 0.7675 |

100 | 0.9860 | 0.7568 |

110 | 0.9981 | 0.7354 |

120 | 0.9585 | 0.5082 |

130 | 0.9082 | 0.3041 |

140 | 0.8911 | 0.2910 |

150 | 0.8683 | 0.3175 |

Once more, we apply the Anderson-Darling and Lilliefors tests to the data to assess the normality. The results from both tests reveal similar numbers of nonnormal and normal distributions. So again we apply the Kruskal-Wallis and ANOVA1 tests of significance.

Table

Entropic measures for the control and traffic nose exposure subjects RR intervals.

Entropy | Mean ± SD | Mean ± SD | ANOVA1 | Kruskal-Wallis | Effect size |
---|---|---|---|---|---|

Approximate | | | | | - |

Sample | | | | | - |

DFA | | | | | - |

Shannon | | | <0.0001 | <0.0001 | 1.13 (large) |

Renyi | | | <0.0001 | <0.0001 | 1.06 (large) |

Tsallis | | | <0.0001 | <0.0001 | 1.14 (large) |

Here again we must complete a multivariate analysis. Shannon entropy has the First Principal Component (PC1) of 0.470, the Second Principal Component (PC2) of 0.258, and the Third Principal Component (PC3) of

Only the first three components need be considered due to the relatively steep scree plot. The cumulative influence as a percentage is 65.4 percent for the PC1 and 95.4 percent for the cumulative total of the PC1 and PC2. Finally, it is 99.3 percent for the cumulative total of the PC1, PC2, and PC3.

PC2 has an influence of 30.0 percent. PC3 has an influence of 3.9 percent. So, Shannon, Renyi, and Tsallis are the optimal and most robust statistically overall combination regarding influencing the correct outcome. This is the case by means of the ANOVA1, Kruskal-Wallis, and the multivariate technique, hence PCA.

Table

The relevant Principal Component Analysis for five entropies and DFA of 31 traffic noise exposure subjects.

Entropy (or DFA) | PC1 | PC2 | PC3 |
---|---|---|---|

Approximate | 0.397 | −0.415 | 0.497 |

Sample | 0.405 | −0.419 | 0.307 |

DFA | 0.007 | 0.700 | 0.708 |

Shannon | 0.470 | 0.258 | −0.245 |

Renyi | 0.485 | 0.187 | −0.200 |

Tsallis | 0.472 | 0.249 | −0.242 |

To provide further evidence regarding the interaction between auditory processing and the autonomic nervous system, we attempted to investigate whether acute exposure to traffic noise influenced the complexity of HRV. As a main outcome, we noticed that the traditional linear indices of HRV were unchanged during traffic noise exposure while some nonlinear approaches evidenced that the complexity of heart rate autonomic control increased during exposure to traffic noise.

In this context, previous studies suggest that noise exposure affects the sympathetic component of heart rate autonomic control [

Yet, an important point to be highlighted in their studies is the limitation of the LF/HF ratio to provide information regarding the sympathetic modulation of heart rate. The sympathovagal balance index that was added to their investigation, calculated by the LF/HF ratio, has been demonstrated to be theoretically flawed and empirically unsupported. Though many criticisms of this measure abound, the most serious concern is that LF index does not represent the sympathetic component. Thus, there is a lack of rationale and/or compelling evidence that its strength in relation to the HF index component would indicate relative strength of vagal and sympathetic signaling. Furthermore, the physiological significance of LF/HF ratio is erroneous and represents a superficial understanding of autonomic regulatory mechanisms [

Equally, Sim et al. [^{2}) and submitted them to self-made traffic noise composed by aircraft and road traffic noise. The authors observed that traffic noise exposure increased SDNN and HF band in absolute units, indicating that traffic noise acutely increased HRV.

Although we did not observe any significant effects of traffic noise on time and frequency domain indices of HRV, we reported significant changes in the nonlinear parameters of HRV during traffic noise exposure. Entropic and chaotic global analysis of HRV revealed that the complexity of heart rate autonomic control increased during traffic noise exposure, suggesting increasing randomness in the system.

According to our findings, Shannon entropy values increased (large effect size) during traffic noise exposure. Entropy is theoretically related to the amount of disorder of particles in a system; if the entropy decreases, the predictability of the process increases and the system becomes less complex [

We also revealed that Renyi entropy values were higher during exposure to traffic noise (large effect size). The Renyi entropy generalizes the Shannon entropy and considers the Shannon entropy as a singular case [

Based on our data, Tsallis entropy analysis confirmed that the complexity of HRV increased during traffic noise exposure and Cohen’s

Our results demonstrated through chaotic global analysis of HRV that CFP3 and CFP6 significantly increased (large effect size) during traffic noise exposure, indicating higher complexity of RR-intervals oscillations during auditory stimulation. A previous study reported that chaotic global analysis was unable to identify HRV changes during mental task [

Nonlinear analysis of HRV is a complex issue owing to its physiological interpretation. Conversely, the literature shows that decreased complexity of HRV represents a physiological impairment. Accordingly, our data points to an interesting interpretation that acute traffic noise exposure in a laboratory situation does not cause stressful autonomic responses. An elegant systematic review reported that the majority of studies performed at the roadside evidenced stressful effects of traffic noise on cardiovascular, respiratory, and metabolic health [

The interaction between auditory processing and heart rate autonomic control has been reviewed before [

Amongst the important points to be addressed in our study, we allow for the laboratory conditions the volunteers were exposed to. This is because we intended to discard the influence of the traffic environmental impact on HRV, that is, pollution, visual stimulation, and conversation. We investigated only women in order to avoid influence of sexual hormones. We believe that a combination of different factors during traffic noise stimulus would induce tougher effects on HRV, since the ANS is sensitive to innumerous exogenous elements [

The luteal and follicular phase of the menstrual cycle were also controlled, since there is previous evidence of its influence on nonlinear HRV [

Another fact worth highlighting is that, in our study, nonlinear methods of HRV were more sensitive at detecting changes in the RR-interval fluctuations. This is possibly because some information may be erroneous if only linear analysis is undertaken. Nonlinear analysis was revealed to be a more powerful approach to identify complex systems [

Traffic noise exposure did not significantly alter linear indices of HRV. Higuchi fractal dimension, DFA, and Approximate and Sample entropies were similarly significantly unaffected. Yet, it significantly changed chaotic global analysis (combinations CFP3 and CFP6) and Shannon, Renyi, and Tsallis entropies. Our results indicate that traffic noise acutely enhances the complexity of heart rate autonomic control in healthy women.

The authors declare that there are no conflicts of interest regarding the publication of this paper.

This study received financial support from FAPESP (Process no. 2012/01366-6 and 2018/02664-7).